Building RAG Systems: From Vector Embeddings to Self-Reflective CRAG - 5 Python Notebooks
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Learn to build advanced Retrieval-Augmented Generation (RAG) systems through five Python notebooks in this 19-minute tutorial video. Progress from implementing basic text embeddings and creating vector stores to developing sophisticated autonomous ReAct Agents and self-reflective C-RAG systems. Master essential RAG concepts including vector embeddings, function calling, and building RAG from scratch using LlamaIndex and Haystack frameworks. Explore practical implementations with downloadable Colab notebooks that demonstrate each concept, from fundamental RAG architectures to advanced self-corrective systems. Gain hands-on experience with Mistral's approach to RAG development while following clear, step-by-step demonstrations of each implementation stage.
Syllabus
RAG IPYNB Overview
Vector Embeddings for RAG
Function Calling for RAG explained
RAG from scratch - LlamaIndex Haystack
ReAct Agent for RAG ipynb
Self-Reflective RAG and CRAG
The IPYNB download
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